An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection
Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playing, thereby enriching propagation with synthetic signals. However, such propagation data is intrinsically unreliable, and directly fusing it can lead to biased representations and limited detection performance. In this paper, we alleviate the unreliability of synthetic propagation from the mutual information perspective and propose a novel information-theoretic propagation denoising and fusion (InfoPDF) framework to learn effective representations from both real and synthetic propagation. Specifically, we first g...
| Authors | Mengyang Chen, Lingwei Wei, Wei Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 May 2026 |
| Last updated at source | 19 August 2026 |
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